The overall objective of our proposal is to develop with the aid of computers, methods for improving the cost effectiveness and accuracy of diagnostic evaluations of patients with arthritic disorders. The method that we propose to use is based upon pattern recognition techniques using multi-membership classification methods which allows for simultaneous existence of several disorders in any given patient. We are currently identifying the relevant features for each disorder and the conditional probabilities for each feature. The diagnostic software consisting of a preliminary model for feature input, posterior probability computation, and feature selection, has been implemented and is being tested. The accuracy of the Arthritis Diagnostic System (ADS) will be tested and validated on separate existing large rheumatic disease data bases. This diagnostic system will be evaluated as to its ability to improve the cost effectiveness of medical care of patients with arthritis. The expense and risk to the patient and the path recommended for diagnosis with the aid of the computer will be compared with the judgment of physicians of various levels of training. Two additional problems which we have encountered will be addressed. First, the method of deriving conditional probabilities from a data base such as our MEDIC. The other is the method for improving feature selection by utilizing clusters of features as opposed to less powerful individual features. This project would greatly enhance physician utilization of computer information systems by amplifying the clinical usefulness of data base systems.